Characterizing users’ check-in activities using their scores in a location-based social network

Jin, Lei; Long, Xuelian; Zhang, Ke; Lin, Yu‐Ru; Joshi, James

doi:10.1007/s00530-014-0395-8

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…For instance, mobile phone datasets have been used to understand the crowd and individual mobility patterns [45][46][47]. However, mobile phone data sets are not the only choice to study human mobility pattern analysis.…”

Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

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Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as "Weibo") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

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Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

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“…The check-in data on mobile social media differ from those in GPS loggers or geotagged photos because they provide semantic tags to the places where users are located rather than merely giving raw geolocations. Check-in data have recently received attention from computer scientists who seek to develop analytical techniques to better understand user activities (Yu et al 2014; Hasan and Ukkusuri 2014; Jin et al 2016) and develop an intelligent recommendation system (Chen et al 2015). In these works, the check-in data were collected from Foursquare, a popular mobile social media platform with more than 60 million users as of 2015 (https://foursquare.com/about).…”

Section: Literature Reviewmentioning

confidence: 99%

Tourist Activity Analysis by Leveraging Mobile Social Media Data

Law

et al. 2017

Journal of Travel Research

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Approaches to traditional travel diary construction rely on tourist participation and manual recording; hence, they are not only time-consuming but also limited in the scale and the number of samples. Online social network platforms have been used as alternative data sources for capturing the movements and travel patterns of tourists at a large scale. However, they fail to provide detailed contextual information on tourist activities for further analysis. In this paper, we present a new approach to travel diary construction based on the venue check-in data available in mobile social media with rich information on locations, time, and activities. Our case study focuses on the inbound tourism in Hong Kong using a data set comprising of 17,355 check-ins generated by 600 tourists. We demonstrate how the proposed travel diary can provide useful practical implications for applications in location management, transportation management, impact management, and tourist experience promotion among others.

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“…Our work can be enclosed in an alternative course of action for OSN-based mobility pattern discovery following a clustering-based approach. Basically, these works cluster the locations or paths followed by OSN users and then, on top of these clusters, make up the eventual mobility patterns [ 48 , 50 , 51 , 55 ]. In that sense, several clustering solutions have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Ref. [ 50 ] envisions a non-negative matrix factorization to cluster profiling information of OSN users related to their activity score within the platform to capture the spatio-temporal features of their consecutive movements across a city.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

Cuenca-Jara

Terroso-Sáenz

Valdés-Vela

et al. 2017

Sensors

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Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

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Characterizing users’ check-in activities using their scores in a location-based social network

Cited by 9 publications

References 16 publications

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Tourist Activity Analysis by Leveraging Mobile Social Media Data

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

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